The Cropland Data Layer (CDL) is a GIS dataset created by the United States Department of Agriculture (USDA) that provides detailed information about the extent and types of crops grown in the United States. The CDL is produced annually.
The History of the Cropland Data Layer
The CDL was first developed in 1997 as part of the USDA’s efforts to improve the accuracy and reliability of agricultural statistics. The early versions of the CDL were based on the use of satellite imagery and other data sources, including ground surveys, weather reports, and crop yield models.
These GIS data sources were used to create detailed maps that showed the location and type of crops grown in the United States.
In 2008, the USDA began to use multi-spectral satellite imagery to create the CDL. This technology allowed for a more accurate and detailed analysis of crop production, and it allowed for more frequent updates to the CDL.
Free weekly newsletter
Fill out your e-mail address to receive our newsletter!
By entering your email address you agree to receive our newsletter and agree with our privacy policy.
You may unsubscribe at any time.
The most recent cropland data sets are derived from Landsat 8, 9 OLI/TIRS, ISRO ResourceSat-2 LISS-3, and ESA Sentinel-2A and -2B imagery and are mapped to a a spatial resolution of 30 meters.
The CDL is updated annually and provides detailed information about more than 100 crops, including major crops such as corn, soybeans, wheat, and cotton, as well as minor crops such as fruits, vegetables, and nuts.
How is the Cropland Data Layer used?
Here are some of the ways that the CDL is used:
Monitoring Crop Production
The CDL provides up-to-date information about crop production across the country. This information can be used to monitor changes in crop patterns, predict yields, and identify areas that may be vulnerable to drought, pests, or other risks. Farmers can use this information to make decisions about planting, harvesting, and marketing their crops.
Improving Crop Management
The CDL can be used to identify areas that may be at risk of soil erosion, nutrient depletion, or other environmental risks. This information can be used to develop more sustainable agricultural practices and protect the long-term health of our soils and ecosystems.
Assessing the Impact of Weather Events
The CDL can be used to assess the impact of weather events such as droughts, floods, and heatwaves on crop production. This information can be used to develop better strategies for managing the risks associated with extreme weather events.
Supporting Agricultural Policy
The CDL provides policymakers with valuable information about the agricultural sector, including the types of crops grown, the amount of land used for farming, and the distribution of crop production across the country. This information can be used to inform policy decisions related to agriculture, trade, and food security.
Advances with the Cropland Data Layer
The CDL has had a significant impact on the agricultural industry and the wider economy.
Here are some of the ways that the CDL has been used to drive innovation and improve outcomes in agriculture:
Precision Agriculture
The CDL has been used to develop new technologies and practices that enable farmers to manage their crops more precisely.
For example, farmers can use the CDL to identify areas of their fields that require more or less irrigation, fertilizer, or pesticides. This information can be used to improve yields and reduce input costs, which can increase profitability and sustainability.
Market Analysis
The CDL has been used by private companies to analyze the agricultural market and develop new products and services. For example, companies that provide crop insurance or commodity trading services can use the CDL to assess the risks associated with different crops and regions, and develop customized products and services that meet the needs of farmers and investors.
Environmental Stewardship
The CDL has played an important role in promoting environmental stewardship in the agricultural sector. By providing detailed information about the location and types of crops grown, the CDL has helped to identify areas that may be at risk of soil erosion, nutrient depletion, or other environmental risks.
This information has been used to develop more sustainable agricultural practices, such as conservation tillage, cover cropping, and precision irrigation, that reduce environmental impacts and preserve the long-term health of our soils and ecosystems.
Improved Policy and Planning:
The CDL has been used to inform policy decisions related to agriculture, trade, and food security. For example, the CDL has been used to assess the impact of trade agreements on U.S. agricultural exports, to evaluate the effectiveness of conservation programs, and to identify areas where investment in rural infrastructure is needed.
Limitations to the Cropland Data Layer
Despite its many benefits, the CDL does have some challenges and limitations. One of the main challenges is the quality and consistency of the data used to create the CDL.
The CDL relies on a combination of satellite imagery, ground surveys, and other data sources, which can be subject to errors, biases, and gaps in coverage. The data accuracy for each year can be found in the metadata that accompanies each cropland dataset.
Additionally, the CDL is not always able to capture the full complexity of agricultural systems, such as mixed-crop and agroforestry systems, which may require different data sources and analysis methods.
Another limitation of the CDL is its reliance on remote sensing technology, which may not always capture the full range of crop variability and yield potential. For example, the CDL may not be able to accurately identify certain types of crops, such as specialty crops, or to capture the impact of local factors such as soil quality, irrigation practices, and pest management.
How to map the Cropland Data Layer (CDL) with GIS
Users can download the Cropland Data Layer (CDL) by year from the Cropland Data Layer page hosted by the USDA’s National Agricultural Statistics Service web site. Cropland data can be accessed by year from 2008 to 2022.
Each year has the data in raster format as a TIFF file.
When you decompress the cropland data set you will notice several file types that are stored in the folder.
To map out crop data with this dataset, load the file with the .tif file extension into your GIS software program. This file is the raster dataset that contains all of the crop data.
Make sure the file with the .tfw extension is stored in the same folder as the TIFF file. The .tfw is a world file that contains the coordinate information needed for your GIS software to properly georeference the data.
Each crop type is stored as a band in the TIFF file which are described in the metadata_Cropland-Data-Layer-2022.htm metadata file that is packaged with the cropland layer. There are over 100 crops that are mapped in this dataset.
To understand what each number means, scroll down to the data dictionary in this HTML file.
The cropland layer maps out more than just crops. The cropland dataset also maps out different levels of urban areas, perennial ice and snow, forest, grasslands, and wetlands.
References
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto International, 26(5), 341-358. https://doi.org/10.1080/10106049.2011.562309
USDA national agricultural statistics service cropland data layer. (2016, November 8). USGS.gov. https://www.usgs.gov/centers/fort-collins-science-center/science/usda-national-agricultural-statistics-service-cropland
Zhang, C., Di, L., Hao, P., Yang, Z., Lin, L., Zhao, H., & Guo, L. (2021). Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer. International Journal of Applied Earth Observation and Geoinformation, 102, 102374. https://doi.org/10.1016/j.jag.2021.102374
